Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques

Squarcina, Letizia; Castellani, Umberto; Bellani, Marcella; Perlini, Cinzia; Lasalvia, Antonio; Dusi, Nicola; Bonetto, Chiara; Cristofalo, Doriana; Tosato, Sarah; Rambaldelli, Gianluca; Alessandrini, Franco; Zoccatelli, Giada; Pozzi-Mucelli, Roberto; Lamonaca, Dario; Castelli, Enrico; Pileggi, Francesca; Mazzi, Fausto; Paolo, Santonastaso; Ruggeri, Mirella; Brambilla, Paolo

doi:10.1016/j.neuroimage.2015.12.007

Cited by 57 publications

(26 citation statements)

References 37 publications

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“…Furthermore, it has been shown that NA-FEP patients have significantly less GM volumes in superior temporal gyri, posterior temporal lobe, and lateral and medial frontal regions in respect to A-FEP (Farrow et al, 2005;Takahashi et al, 2009a). Overall, these evidences are in line with previous MRI findings reporting widespread GM abnormalities in SCZ, particularly in fronto-temporal regions (Chan et al, 2009;Kim et al, 2017;Squarcina et al, 2017a;Squarcina et al, 2015), which are crucial to coordinate executive functions, and language processes (Sumich et al, 2005). Additionally, two studies observed smaller GM volumes in cingulate gyrus in NA-FEP (Koo et al, 2008) and A-FEP (Morgan et al, 2007) compared to healthy controls, whereas no significant differences were found between the two patient groups.…”

Section: Accepted Manuscriptsupporting

confidence: 89%

Gray matter differences between affective and non-affective first episode psychosis: A review of Magnetic Resonance Imaging studies

Calvo

Delvecchio

Altamura

et al. 2019

Journal of Affective Disorders

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Section: Accepted Manuscriptsupporting

confidence: 89%

Gray matter differences between affective and non-affective first episode psychosis: A review of Magnetic Resonance Imaging studies

Calvo

Delvecchio

Altamura

et al. 2019

Journal of Affective Disorders

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“…Within the field of biological psychiatry, there is growing interest in the application of machine learning (ML) techniques to neuroimaging data for the diagnosis of psychiatric illness (Arbabshirani, Castro, & Calhoun, ; Kim, Calhoun, Shim, & Lee, ; Orru, Pettersson‐Yeo, Marquand, Sartori, & Mechelli, ), and the prediction of disease transition in individuals at clinical high risk (Chung et al, ; Koutsouleris et al, ; Pettersson‐Yeo et al, ). Over the past decade, psychiatric disorders such as schizophrenia have been the focus of much research on automatic diagnosis by the integration of ML and neuroimaging (Squarcina et al, ; Valli et al, ; Zarogianni, Moorhead, & Lawrie, ). The vast majority of the existing studies have applied ML techniques to a single neuroimaging modality including structural magnetic resonance imaging (sMRI) (Borgwardt et al, ; Koutsouleris et al, ; Schnack et al, ), resting‐state functional magnetic resonance imaging (fMRI) (Chyzhyk, Grana, Ongur, & Shinn, ; S. Wang et al, ) or task‐related fMRI (Bendfeldt et al, ; Costafreda et al, ).…”

Section: Introductionmentioning

confidence: 99%

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

Lei

Pinaya

Young

et al. 2019

Human Brain Mapping

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Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

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“…1,2 Applications of cutting-edge machine learning approaches in structural neuroimaging studies have revealed potential pathways to classification of schizophrenia based on regional gray matter volume (GMV) or density or cortical thickness. [3][4][5] Additionally, cortical folding may have high discriminatory value in correctly identifying symptom severity in schizophrenia. 6 Regional GMV and cortical thickness have also been combined in attempts to differentiate individuals with schizophrenia from healthy controls (HCs).…”

Section: Introductionmentioning

confidence: 99%

Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion Imaging Study

Liang

Zhang

et al. 2018

Schizophrenia Bulletin

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Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia.

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Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques

Cited by 57 publications

References 37 publications

Gray matter differences between affective and non-affective first episode psychosis: A review of Magnetic Resonance Imaging studies

Gray matter differences between affective and non-affective first episode psychosis: A review of Magnetic Resonance Imaging studies

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion Imaging Study

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